Owing to the difficulty of utilizing hidden spatio-temporal information, spatio-temporal knowledge graph (KG) reasoning tasks in real geographic environments have issues of low accuracy and poor interpretability. This paper proposes a grid neighborhood-based graph convolutional network (GN-GCN) for spatio-temporal KG reasoning. Based on the discretized process of encoding spatio-temporal data through the GeoSOT global grid model, the GN-GCN consists of three parts: a static graph neural network, a neighborhood grid calculation, and a time evolution unit, which can learn semantic knowledge, spatial knowledge, and temporal knowledge, respectively. The GN-GCN can also improve the training accuracy and efficiency of the model through the multiscale aggregation characteristic of GeoSOT and can visualize different probabilities in a spatio-temporal intentional probabilistic grid map. Compared with other existing models (RE-GCN, CyGNet, RE-NET, etc.), the mean reciprocal rank (MRR) of GN-GCN reaches 48.33 and 54.06 in spatio-temporal entity and relation prediction tasks, increased by 6.32/18.16% and 6.64/15.67% respectively, which achieves state-of-the-art (SOTA) results in spatio-temporal reasoning. The source code of the project is available at https://doi.org/10.18170/DVN/UIS4VC.

中文翻译：

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GN-GCN： 基于网格邻域的图卷积网络，用于时空知识图谱推理


由于隐藏时空信息的利用难度，真实地理环境中的时空知识图谱 （KG） 推理任务存在准确性低、可解释性差的问题。该文提出了一种基于网格邻域的图卷积网络（GN-GCN），用于时空KG推理。GN-GCN 基于通过 GeoSOT 全局网格模型对时空数据进行离散编码的过程，由静态图神经网络、邻域网格计算和时间演化单元三部分组成，分别可以学习语义知识、空间知识和时间知识。GN-GCN 还可以通过 GeoSOT 的多尺度聚合特性提高模型的训练精度和效率，并且可以在时空意向概率网格图中可视化不同的概率。与其他现有模型（RE-GCN、CyGNet、RE-NET等）相比，GN-GCN在时空实体和关系预测任务中的平均倒数秩（MRR）达到48.33和54.06，分别提高了6.32%/18.16%和6.64/15.67%，在时空推理中取得了最先进的（SOTA）结果。该项目的源代码可在 https://doi.org/10.18170/DVN/UIS4VC 上获得。